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Windfarm detection based on Sentinel-1 imagery and deep learning techniques

Diehl, Frederik (2019) Windfarm detection based on Sentinel-1 imagery and deep learning techniques. Master's, Universität Trier.

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Item URL in elib:https://elib.dlr.de/123868/
Document Type:Thesis (Master's)
Title:Windfarm detection based on Sentinel-1 imagery and deep learning techniques
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Diehl, FrederikUNSPECIFIEDUNSPECIFIED
Date:2019
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:98
Status:Published
Keywords:Deep learning, Sentinel-1, wind farm detection, coastal application
Institution:Universität Trier
Department:Umweltfernerkundung und Geoinformatik
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Deposited By: Huth, Juliane
Deposited On:26 Nov 2019 12:14
Last Modified:26 Nov 2019 12:14

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